Currently, social networks present information of great relevance to various government agencies and different types of companies, which need knowledge insights for their business strategies. From this point of view, an important technique for data analysis is to create and maintain an environment for collecting data and transforming them into intelligence information to enable analysts to observe the evolution of a given topic, elaborate the analysis hypothesis, identify botnets, and generate data to aid in the decision-making process. Focusing on collecting, analyzing, and supporting decision-making, this paper proposes an architecture designed to monitor and perform anonymous real-time searches in tweets to generate information allowing sentiment analysis on a given subject. Therefore, a technological structure and its implementation are defined, followed by processes for data collection and analysis. The results obtained indicate that the proposed solution provides a high capacity to collect, process, search, analyze, and view a large number of tweets in several languages, in real-time, with sentiment analysis capabilities, at a low cost of implementation and operation.

The malicious code analysis allows malware behavior characteristics to be identified, in other words how does it act in the operating system, what obfuscation techniques are used, which execution flows lead to the primary planned behavior, use of network operations, files downloading operations, user and system's information capture, access to records, among other activities, in order to learn how malware works, to create ways to identify new malicious softwares with similar behavior, and ways of defense. Manual scanning for signature generation becomes impractical, since it requires a lot of time compared to new malwares' dissemination and creation speed. Therefore, this paper proposes the use of sandbox techniques and machine learning techniques to automate software identification in this context. This paper, besides presenting a different and faster approach to malware detection, has achieved an accuracy rate of over 90% for the task of malware identifying.

The increasing sophistication of phishing attacks represents a significant challenge to cybersecurity, particularly in the context of fraudulent emails crafted to deceive users and steal confidential information. This paper proposes a deep learning–based approach for the automatic detection of phishing in emails, the PHILDER (PHishing Intelligent Lightweight DEtection on Resource-limited devices) model. The study employs a dataset composed of real emails extracted from PhishTank and the SpamAssassin Public Corpus, spanning both legitimate and fraudulent samples. To address the natural imbalance between spam and phishing emails, two strategies were applied: undersampling, which reduces the number of legitimate emails to balance the classes, and oversampling using SMOTE (Synthetic Minority Over-sampling Technique) to generate new synthetic samples of the minority class. Five models (ALBERT, DistilBERT, MobileBERT, MiniLM, and TinyBERT) were trained: one using the original dataset, one with undersampling, and a third with oversampling. The experimental results show that the oversampling approach had to be discarded due to computational cost issues, the tendency to overfit, and inferior performance compared to the other two approaches. The main contributions of this paper include the application of computational efficiency metrics for model evaluation, which not only consider traditional metrics but also enable assessment in real-world applications under hardware computational constraints, and the exploration of different data composition strategies for training. The use of PHILDER (TinyBERT combined with data balancing techniques) demonstrates a promising approach for the automatic detection of phishing in emails, contributing to strengthening digital security against social engineering attacks.

After detecting a malicious artifact, classifying malware into specific families becomes an essential step to understand the threat's behavior, implement mitigation strategies, and develop proactive defenses. This task is particularly challenging due to the diversity of malware formats, the rapid evolution of obfuscation and packing techniques, as well as the scarcity of labeled data for training robust models. Additionally, the high volume of samples generated daily demands solutions that combine high accuracy and computational efficiency. Although transformer-based models are widely recognized as the state-of-the-art for sequence processing tasks, their high computational demands limit their practical application in resource-constrained environments. In this work, we present FORTUNATE, a lightweight framework that leverages LSTM networks with one-hot encoding to classify malware based on variable-length opcode sequences. The framework adopts an optimized opcode extraction process focused on reducing redundancies and representing data in compact vectors, minimizing computational costs. Experimental results indicate that FORTUNATE achieves accuracies of 99.82% for active malware and 99.81% for inactive malware, with an average classification time of only 56 ms per sample, significantly outperforming related works. The obtained results demonstrate that lightweight artificial intelligence approaches can deliver competitive performance in malware classification, especially in scenarios with computational constraints. FORTUNATE not only fills an important gap in malware classification but also establishes a foundation for future research aimed at optimizing the balance between accuracy, efficiency, and scalability.

The increasing complexity of contemporary malware, driven by the use of advanced evasion and obfuscation techniques, combined with the rapid growth in the number of new variants emerging continuously, undermines the effectiveness of traditional signature-based detection mechanisms. In response to this scenario, this work proposes FORENSICS (Framework fOr malwaRe dEtectioN baSed on InstruCtion Sequences), an innovative deep learning-based framework that employs variable-length instruction sequences to detect malware efficiently and accurately. By integrating Natural Language Processing (NLP) techniques with Long Short-Term Memory (LSTM) neural networks, FORENSICS analyzes opcode sequences extracted from real-world malware and benign software artifacts. The framework introduces optimized methods for opcode extraction and representation, significantly reducing computational overhead while preserving detection performance. FORENSICS achieved 99.91% accuracy, 99.99% precision, and detection times ranging from 8 to 17 milliseconds, outperforming several state-of-the-art approaches across multiple metrics. Additionally, the framework demonstrated robustness in identifying zero-day malware samples, confirming its effectiveness in real-world cybersecurity scenarios. A new balanced dataset comprising over 40,000 labeled samples was created and made publicly available, facilitating reproducibility and encouraging further research. These results position FORENSICS as a robust, scalable, and highly effective solution for malware detection in modern threat landscapes.